mirror of https://github.com/hpcaitech/ColossalAI
268 lines
11 KiB
Python
268 lines
11 KiB
Python
from functools import partial
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from typing import Callable, Dict, List, Union
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import torch.nn as nn
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from torch import Tensor
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from torch.nn import Module
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from colossalai.shardformer.layer import FusedRMSNorm, Linear1D_Col, Linear1D_Row, VocabParallelEmbedding1D
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from ..modeling.llama import LlamaPipelineForwards, get_llama_flash_attention_forward
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from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription
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__all__ = ['LlamaPolicy', 'LlamaForCausalLMPolicy', 'LlamaForSequenceClassificationPolicy']
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class LlamaPolicy(Policy):
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def config_sanity_check(self):
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pass
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def preprocess(self):
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if self.shard_config.enable_tensor_parallelism:
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# Resize embedding
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vocab_size = self.model.config.vocab_size
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world_size = self.shard_config.tensor_parallel_size
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if vocab_size % world_size != 0:
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new_vocab_size = vocab_size + world_size - vocab_size % world_size
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self.model.resize_token_embeddings(new_vocab_size)
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return self.model
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def module_policy(self) -> Dict[Union[str, nn.Module], ModulePolicyDescription]:
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from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaModel
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policy = {}
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if self.shard_config.enable_tensor_parallelism:
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policy[LlamaDecoderLayer] = ModulePolicyDescription(
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attribute_replacement={
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"self_attn.hidden_size":
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self.model.config.hidden_size // self.shard_config.tensor_parallel_size,
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"self_attn.num_heads":
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self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size,
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},
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sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="self_attn.q_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="self_attn.k_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="self_attn.v_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="self_attn.o_proj",
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target_module=Linear1D_Row,
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),
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SubModuleReplacementDescription(
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suffix="mlp.gate_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="mlp.up_proj",
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target_module=Linear1D_Col,
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),
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SubModuleReplacementDescription(
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suffix="mlp.down_proj",
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target_module=Linear1D_Row,
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)
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],
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)
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="embed_tokens",
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target_module=VocabParallelEmbedding1D,
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),
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policy=policy,
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target_key=LlamaModel)
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# optimization configuration
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if self.shard_config.enable_fused_normalization:
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self.append_or_create_submodule_replacement(description=[
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SubModuleReplacementDescription(
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suffix="input_layernorm",
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target_module=FusedRMSNorm,
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),
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SubModuleReplacementDescription(
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suffix="post_attention_layernorm",
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target_module=FusedRMSNorm,
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)
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],
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policy=policy,
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target_key=LlamaDecoderLayer)
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self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription(
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suffix="norm",
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target_module=FusedRMSNorm,
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),
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policy=policy,
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target_key=LlamaModel)
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if self.shard_config.enable_flash_attention:
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self.append_or_create_method_replacement(description={
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'forward': get_llama_flash_attention_forward(),
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},
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policy=policy,
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target_key=LlamaAttention)
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return policy
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def postprocess(self):
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return self.model
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def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None:
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"""If under pipeline parallel setting, replacing the original forward method of huggingface
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to customized forward method, and add this changing to policy."""
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if self.pipeline_stage_manager:
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stage_manager = self.pipeline_stage_manager
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if self.model.__class__.__name__ == "LlamaModel":
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module = self.model
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else:
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module = self.model.model
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layers_per_stage = Policy.distribute_layers(len(module.layers), stage_manager.num_stages)
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stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage)
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method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)}
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self.append_or_create_method_replacement(description=method_replacement,
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policy=policy,
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target_key=model_cls)
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return
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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assert self.pipeline_stage_manager is not None
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if self.model.__class__.__name__ == 'LlamaModel':
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module = self.model
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else:
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module = self.model.model
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stage_manager = self.pipeline_stage_manager
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held_layers = []
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layers_per_stage = self.distribute_layers(len(module.layers), stage_manager.num_stages)
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if stage_manager.is_first_stage():
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held_layers.append(module.embed_tokens)
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start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage)
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held_layers.extend(module.layers[start_idx:end_idx])
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if stage_manager.is_last_stage():
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held_layers.append(module.norm)
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return held_layers
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class LlamaModelPolicy(LlamaPolicy):
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def __init__(self) -> None:
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super().__init__()
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def module_policy(self):
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policy = super().module_policy()
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from transformers.models.llama.modeling_llama import LlamaModel
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if self.pipeline_stage_manager:
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# set None as default
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self.set_pipeline_forward(model_cls=LlamaModel,
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new_forward=LlamaPipelineForwards.llama_model_forward,
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policy=policy)
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return policy
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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held_layers = super().get_held_layers()
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in llama model"""
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return []
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class LlamaForCausalLMPolicy(LlamaPolicy):
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def module_policy(self):
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from transformers import LlamaForCausalLM
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policy = super().module_policy()
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if self.shard_config.enable_tensor_parallelism:
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# add a new item for casual lm
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new_item = {
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LlamaForCausalLM:
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ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="lm_head", target_module=Linear1D_Col, kwargs=dict(gather_output=True))
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])
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}
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policy.update(new_item)
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if self.pipeline_stage_manager:
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# set None as default
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self.set_pipeline_forward(model_cls=LlamaForCausalLM,
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new_forward=LlamaPipelineForwards.llama_for_causal_lm_forward,
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policy=policy)
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return policy
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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stage_manager = self.pipeline_stage_manager
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held_layers = super().get_held_layers()
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if stage_manager.is_last_stage():
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held_layers.append(self.model.lm_head)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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llama_model = self.model.model
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if self.pipeline_stage_manager and self.pipeline_stage_manager.num_stages > 1:
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if id(llama_model.embed_tokens.weight) == id(
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self.model.lm_head.weight) and self.pipeline_stage_manager.num_stages > 1:
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# tie weights
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return [{
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0: llama_model.embed_tokens.weight,
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self.pipeline_stage_manager.num_stages - 1: self.model.lm_head.weight
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}]
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return []
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class LlamaForSequenceClassificationPolicy(LlamaPolicy):
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def module_policy(self):
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from transformers import LlamaForSequenceClassification
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policy = super().module_policy()
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if self.shard_config.enable_tensor_parallelism:
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# add a new item for sequence classification
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new_item = {
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LlamaForSequenceClassification:
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ModulePolicyDescription(sub_module_replacement=[
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SubModuleReplacementDescription(
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suffix="score", target_module=Linear1D_Col, kwargs=dict(gather_output=True))
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])
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}
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policy.update(new_item)
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# to be confirmed
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if self.pipeline_stage_manager:
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# set None as default
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self.set_pipeline_forward(model_cls=LlamaForSequenceClassification,
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new_forward=LlamaPipelineForwards.llama_for_sequence_classification_forward,
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policy=policy)
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return policy
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def get_held_layers(self) -> List[Module]:
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"""Get pipeline layers for current stage."""
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stage_manager = self.pipeline_stage_manager
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held_layers = super().get_held_layers()
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if stage_manager.is_last_stage():
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held_layers.append(self.model.score)
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return held_layers
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def get_shared_params(self) -> List[Dict[int, Tensor]]:
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"""No shared params in llama for sequence classification model"""
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return []
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